中华口腔医学杂志最新文献

Craniofacial fibrous dysplasia (CFD) is a rare skeletal disorder characterized by the abnormal replacement of normal bone tissue with fibrous tissue. This article provides a systematic review of the latest advancements in the genetic basis, molecular mechanisms, clinical manifestations, and diagnostic and therapeutic strategies of CFD. Elucidate, which leads to bone homeostasis imbalance and fibrotic abnormalities. It focuses on the molecular mechanisms underlying multi-pathway network dysregulation induced by GNAS gene mutations and explores the roles of key molecules like cAMP-response element binding protein, interleukin-6 and Fibroblast growth factor 23 in disease progression. Additionally, it evaluates the limitations of traditional treatments and the translational potential of novel strategies, including targeted therapies, offering a theoretical foundation for clinical practice and future research directions.

Objective: To validate the accuracy of three-dimensional anatomical models reconstructed from cone beam CT (CBCT) using micro-CT as the gold standard, and to evaluate the feasibility of performing anatomical analyses on such models. Methods: A total of 13 isolated deciduous teeth with intact roots were collected, including 5 anterior teeth and 8 molars, with a total of 34 root canals. The teeth were extracted from children aged 3-9 years who visited Peking University Hospital of Stomatology from January 2019 to April 2022 due to trauma or periapical disease, and were then scanned by micro-CT (with a voxel size of 0.018 mm) and CBCT (with a voxel size of 0.125 mm), respectively. Using a threshold-based semi-automated region segmentation method, anatomical models of these isolated teeth were reconstructed from the two CTs. Subsequently, the two CT reconstructed models were registered based on the iterative closest point algorithm, followed by deviation analysis. The key anatomical parameters were measured on the micro-CT and CBCT models, respectively, and the differences were calculated. Results: The CBCT reconstruction models were relatively accurate in the hard tissue morphology, and 97.1% (33/34) of the root canals were identified accurately. When it comes to the deviation analysis, the average distance between the matched points on the CBCT reconstruction models and the micro-CT models was (0.01±0.03) mm for the hard tissue, and (0.00±0.03) mm for the pulp chambers and canals, which did not affect clinical observation. The hard tissue and root canal length of CBCT models were both smaller than those of micro-CT models (P<0.05), with a 95% limits of agreement of (-0.70, 0.14) mm for the hard tissue, and a 95% limits of agreement of (-1.93, 1.00) mm for the pulp chambers and canals. The impact of these differences on clinical operations was all within the acceptable range. Conclusions: Using micro-CT as a validate standard, CBCT with a voxel size of 0.125 mm was proved to be an effective tool for the reconstruction of deciduous teeth. Therefore, the reconstructed models were appropriate for studying deciduous teeth anatomy.

Temporomandibular disorders (TMD) is one of the most clinically common oral diseases.It covers a range of conditions involving the temporomandibular joint (TMJ) and its adjacent muscles and tissues. Patients typically experience discomfort symptoms such as joint clicking, TMJ pain, chewing muscle discomfort, and jaw movement dysfunction. Given the complexity of the etiology of TMD, the diversity and non-specificity of symptoms, how to accurately diagnose and formulate the best treatment plan has become a challenging issue that urgently needs to be addressed in clinical practice. In recent years, the rapid development of artificial intelligence technology, especially deep learning (DL) technology, has brought a revolutionary driving force to medical diagnosis and treatment. This article aims to review the current application of DL in the diagnosis and treatment of TMD, discuss the challenges faced, and provide prospects for future development, in order to have a deeper understanding and reflection on the application of DL in the diagnosis and treatment of TMD.

Tooth fragment reattachment is a conservative approach for the restorations of permanent teeth with crown fracture. Due to its high technical sensitivity, the clinical procedures and performances of fragment reattachment have long been the foci of dental clinicians. This article summarizes the indication selection, treatment strategies, and clinical procedure of fragment reattachment, and introduces the new concepts of this technique in recent years, aiming to provide guidance for the clinical application of fragment reattachment.

Objective: To translate and adapt the postoperative recovery in children (PRiC) scale, developing a Chinese version for children undergoing dental treatment under general anesthesia (PRiC-DTGA) with validated psychometric properties. Methods: The PRiC scale underwent forward-backward translation using Brislin's model. A convenience sample of DTGA patients from the Department of Anesthesiology, School of Stomatology, The Fourth Mility Force Medical University was enrolled for a cross-sectional survey on postoperative complications. Delphi expert consultation informed cultural adaptation based on survey findings to develop the PRiC-DTGA Chinese version. Psychometric validation included reliability and validity testing in a separate DTGA cohort at the same center (April-October 2024). Results: Results from the cross-sectionalsurvey of 231 children showed that 82.7% (191/231) of them hadat least one postoperative complication within 72 hours, and these complications were mainly mild local symptoms. Additionally, 358 copies of the Chinese version of the PRiC-DTGA scale were distributed; 21 invalid questionnaires with incomplete information were excluded, and a total of 337 cases were included inthe study. The final PRiC-DTGA comprised 22 items across three dimensions including physical comfort, social ability, and negative emotional. Exploratory factor analysis (EFA) confirmed all factor loadings>0.4. Confirmatory factor analysis (CFA) demonstrated adequate fit: χ²/df=1.665, tucker-Lewis index (TLI)=0.924, comparative fit index (CFI)=0.896, standardized root mean square residual (SRMR)=0.041, and root mean square error of approximation (RMSEA)=0.044 (90% CI: 0.035-0.053). Reliability was strong with Cronbach's α (total scale)=0.853, subscale α=0.632-0.723, split-half reliability=0.824. Validity indices met standards: scale-content validity index (S-CVI)=0.909, Item-CVI range=0.944-1.000, average variance extracted (AVE)=0.473-0.501, composite reliability (CR)=0.830-0.913. Conclusions: The systematically adapted PRiC-DTGA demonstrates robust reliability and validity, serving as an effective tool for assessing postoperative recovery quality in Chinese children following DTGA.